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News/Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization
arxiv
PublishedApril 18, 2026 at 4:00 AM
▲bullish

Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.14267v1 Announce Type: new Abstract: Search agents extend Large Language Models (LLMs) beyond static parametric knowledge by enabling access to up-to-date and long-tail information unavailable during pretraining. While reinforcement learning has been widely adopted for training such agent

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Discussion
Mentioned models
03
  • 01
    LLM
  • 02
    Qwen3-8B
  • 03
    Qwen3-1.7B
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#reinforcement-learning#search-agents#language-models

No replies yet. Be first.

Mentioned models
03
  • 01
    LLM
  • 02
    Qwen3-8B
  • 03
    Qwen3-1.7B
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#reinforcement-learning#search-agents#language-models
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Originally published on arxiv ↗
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